Triple
T2355237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meyrin |
E47537
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Satigny |
E11133
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Satigny | Statement: [Meyrin, borders, Satigny]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Satigny Context triple: [Meyrin, borders, Satigny]
-
A.
Satigny
chosen
Satigny is a Swiss municipality in the canton of Geneva, known for being one of the country’s largest wine-producing communes.
-
B.
Martigny
Martigny is a historic town in southwestern Switzerland known as a cultural and transportation hub in the canton of Valais, near the Great St. Bernard Pass.
-
C.
Bardonnex
Bardonnex is a small Swiss municipality located in the canton of Geneva, near the country’s border with France.
-
D.
Saignelégier
Saignelégier is a municipality in the Swiss canton of Jura known for its rural landscapes, watchmaking heritage, and the annual Marché-Concours horse festival.
-
E.
Saint-Prex
Saint-Prex is a picturesque medieval town on the shores of Lake Geneva in the canton of Vaud, Switzerland, known for its historic old town and lakeside setting.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a88a1b678c8190bce986922ba60ce0 |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abc6fd4e488190b763a1c9b5d18f2c |
completed | March 7, 2026, 6:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af905b6cfc8190a7c3b51f121cdb18 |
completed | March 10, 2026, 3:30 a.m. |
Created at: March 4, 2026, 7:54 p.m.